In this article, we will see how we can implement bernsen local thresholding in mahotas. Bernsen’s method is one of locally adaptive binarization methods developed for image segmentation. In this study, Bernsen’s locally adaptive binarization method is implemented and then tested for different grayscale images.
In this tutorial, we will use “luispedro” image, below is the command to load it.
mahotas.demos.load('luispedro')
Below is the luispedro image
In order to do this we will use mahotas.thresholding.bernsen method
Syntax : mahotas.thresholding.bernsen(image, contrast_threshold, global_threshold)
Argument : It takes image object and two integer as argument
Return : It returns image object
Note: Input image should be filtered or should be loaded as grey
In order to filter the image we will take the image object which is numpy.ndarray and filter it with the help of indexing, below is the command to do this
image = image[:, :, 0]
Example 1:
Python3
# importing required librariesimport mahotasimport mahotas.demosimport numpy as npfrom pylab import imshow, gray, showfrom os import path# loading the imagephoto = mahotas.demos.load('luispedro')# loading image as greyphoto = mahotas.demos.load('luispedro', as_grey = True)# converting image type to unit8# because as_grey returns floating valuesphoto = photo.astype(np.uint8)# showing original imageprint("Image")imshow(photo)show()# bernsen thresholdphoto = mahotas.thresholding.bernsen(photo, 7, 200)print("Image with bernsen threshold")# showing imageimshow(photo)show() |
Output :
Example 2:
Python3
# importing required librariesimport mahotasimport numpy as npfrom pylab import imshow, showimport os# loading imageimg = mahotas.imread('dog_image.png') # setting filter to the imageimg = img[:, :, 0]print("Image")# showing the imageimshow(img)show()# bernsen thresholdimg = mahotas.thresholding.bernsen(img, 5, 100)print("Image with bernsen threshold")# showing imageimshow(img)show() |
Output :
